






'''simple docstring'''







import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCAmelCase        =							logging.getLogger(__name__)




@dataclass
class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  _a  : str      			=     field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
  _a  : Optional[str]      			=     field(
      default=__A							,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
  _a  : Optional[str]      			=     field(
      default='''NER'''							,       metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}       )
  _a  : Optional[str]      			=     field(
      default=__A							,       metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
  _a  : bool      			=     field(default=__A							,       metadata={'''help''': '''Set this flag to use fast tokenization.'''}       )
  # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
  # or just modify its tokenizer_config.json.
  _a  : Optional[str]      			=     field(
      default=__A							,       metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,       )





@dataclass
class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  _a  : str      			=     field(
      metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}       )
  _a  : Optional[str]      			=     field(
      default=__A							,       metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}							,       )
  _a  : int      			=     field(
      default=128							,       metadata={
          '''help''': (
              '''The maximum total input sequence length after tokenization. Sequences longer '''
              '''than this will be truncated, sequences shorter will be padded.'''
          )
      }							,       )
  _a  : bool      			=     field(
      default=__A							,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





def       __UpperCamelCase					(      ):
       """simple docstring"""




       a_						     =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
       if len(sys.argv   ) == 2 and sys.argv[1].endswith('.json'   ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              a_     ,	a_     ,	a_						     =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
       else:
              a_     ,	a_     ,	a_						     =			parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir   )
           and os.listdir(training_args.output_dir   )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
                  ' --overwrite_output_dir to overcome.'   )

       a_						     =			import_module('tasks'   )
       try:
              a_						     =			getattr(lowercase_   ,       model_args.task_type   )
              a_						     =			token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  F'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
                  F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'   )

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,       datefmt='%m/%d/%Y %H:%M:%S'   ,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,       )
       logger.warning(
           'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'   ,       training_args.local_rank   ,       training_args.device   ,       training_args.n_gpu   ,       bool(training_args.local_rank != -1   )   ,       training_args.fpaa   ,       )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank   ):
              transformers.utils.logging.set_verbosity_info()
              transformers.utils.logging.enable_default_handler()
              transformers.utils.logging.enable_explicit_format()
       logger.info('Training/evaluation parameters %s'   ,       lowercase_   )

       # Set seed
       set_seed(training_args.seed   )

       # Prepare CONLL-2003 task
       a_						     =			token_classification_task.get_labels(data_args.labels   )
       a_						     =			dict(enumerate(lowercase_   )   )
       a_						     =			len(lowercase_   )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       a_						     =			AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,       num_labels=lowercase_   ,       idalabel=lowercase_   ,       labelaid={label: i for i, label in enumerate(lowercase_   )}   ,       cache_dir=model_args.cache_dir   ,       )
       a_						     =			AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,       cache_dir=model_args.cache_dir   ,       use_fast=model_args.use_fast   ,       )
       a_						     =			AutoModelForTokenClassification.from_pretrained(
           model_args.model_name_or_path   ,       from_tf=bool('.ckpt' in model_args.model_name_or_path   )   ,       config=lowercase_   ,       cache_dir=model_args.cache_dir   ,       )

       # Get datasets
       a_						     =			(
           TokenClassificationDataset(
               token_classification_task=lowercase_   ,       data_dir=data_args.data_dir   ,       tokenizer=lowercase_   ,       labels=lowercase_   ,       model_type=config.model_type   ,       max_seq_length=data_args.max_seq_length   ,       overwrite_cache=data_args.overwrite_cache   ,       mode=Split.train   ,       )
           if training_args.do_train
           else None
       )
       a_						     =			(
           TokenClassificationDataset(
               token_classification_task=lowercase_   ,       data_dir=data_args.data_dir   ,       tokenizer=lowercase_   ,       labels=lowercase_   ,       model_type=config.model_type   ,       max_seq_length=data_args.max_seq_length   ,       overwrite_cache=data_args.overwrite_cache   ,       mode=Split.dev   ,       )
           if training_args.do_eval
           else None
       )

       def align_predictions(lowercase_       :  np.ndarray   ,       lowercase_       :  np.ndarray   ) -> Tuple[List[int], List[int]]:
              a_						     =			np.argmax(lowercase_   ,       axis=2   )

              a_     ,	a_						     =			preds.shape

              a_						     =			[[] for _ in range(lowercase_   )]
              a_						     =			[[] for _ in range(lowercase_   )]

              for i in range(lowercase_   ):
                     for j in range(lowercase_   ):
                            if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                                   out_label_list[i].append(label_map[label_ids[i][j]]   )
                                   preds_list[i].append(label_map[preds[i][j]]   )

              return preds_list, out_label_list

       def compute_metrics(lowercase_       :  EvalPrediction   ) -> Dict:
              a_     ,	a_						     =			align_predictions(p.predictions   ,       p.label_ids   )
              return {
                  "accuracy_score": accuracy_score(lowercase_   ,       lowercase_   ),
                  "precision": precision_score(lowercase_   ,       lowercase_   ),
                  "recall": recall_score(lowercase_   ,       lowercase_   ),
                  "f1": fa_score(lowercase_   ,       lowercase_   ),
              }

       # Data collator
       a_						     =			DataCollatorWithPadding(lowercase_   ,       pad_to_multiple_of=8   ) if training_args.fpaa else None

       # Initialize our Trainer
       a_						     =			Trainer(
           model=lowercase_   ,       args=lowercase_   ,       train_dataset=lowercase_   ,       eval_dataset=lowercase_   ,       compute_metrics=lowercase_   ,       data_collator=lowercase_   ,       )

       # Training
       if training_args.do_train:
              trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_process_zero():
                     tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
       a_						     =			{}
       if training_args.do_eval:
              logger.info('*** Evaluate ***'   )

              a_						     =			trainer.evaluate()

              a_						     =			os.path.join(training_args.output_dir   ,       'eval_results.txt'   )
              if trainer.is_world_process_zero():
                     with open(lowercase_   ,       'w'   ) as writer:
                            logger.info('***** Eval results *****'   )
                            for key, value in result.items():
                                   logger.info('  %s = %s'   ,       lowercase_   ,       lowercase_   )
                                   writer.write('%s = %s\n' % (key, value)   )

                     results.update(lowercase_   )

    # Predict
       if training_args.do_predict:
              a_						     =			TokenClassificationDataset(
                  token_classification_task=lowercase_   ,       data_dir=data_args.data_dir   ,       tokenizer=lowercase_   ,       labels=lowercase_   ,       model_type=config.model_type   ,       max_seq_length=data_args.max_seq_length   ,       overwrite_cache=data_args.overwrite_cache   ,       mode=Split.test   ,       )

              a_     ,	a_     ,	a_						     =			trainer.predict(lowercase_   )
              a_     ,	a_						     =			align_predictions(lowercase_   ,       lowercase_   )

              a_						     =			os.path.join(training_args.output_dir   ,       'test_results.txt'   )
              if trainer.is_world_process_zero():
                     with open(lowercase_   ,       'w'   ) as writer:
                            for key, value in metrics.items():
                                   logger.info('  %s = %s'   ,       lowercase_   ,       lowercase_   )
                                   writer.write('%s = %s\n' % (key, value)   )

        # Save predictions
              a_						     =			os.path.join(training_args.output_dir   ,       'test_predictions.txt'   )
              if trainer.is_world_process_zero():
                     with open(lowercase_   ,       'w'   ) as writer:
                            with open(os.path.join(data_args.data_dir   ,       'test.txt'   )   ,       'r'   ) as f:
                                   token_classification_task.write_predictions_to_file(lowercase_   ,       lowercase_   ,       lowercase_   )

       return results


def       __UpperCamelCase					(      lowercase_       :  str   ):
       """simple docstring"""




       main()


if __name__ == "__main__":
  main()












'''simple docstring'''







from math import isqrt, loga


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[True] * max_number
       for i in range(2   ,       isqrt(max_number - 1   ) + 1   ):
              if is_prime[i]:
                     for j in range(i**2   ,       lowercase_   ,       lowercase_   ):
                            a_						     =			False

       return [i for i in range(2   ,       lowercase_   ) if is_prime[i]]


def       __UpperCamelCase					(      lowercase_       :  int = 800_800   ,       lowercase_       :  int = 800_800   ):
       """simple docstring"""




       a_						     =			degree * loga(lowercase_   )
       a_						     =			int(lowercase_   )
       a_						     =			calculate_prime_numbers(lowercase_   )

       a_						     =			0
       a_						     =			0
       a_						     =			len(lowercase_   ) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]   )
                  + prime_numbers[left] * loga(prime_numbers[right]   )
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
  print(f"""{solution() = }""")




